Journal
WATER
Volume 11, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/w11091874
Keywords
peak demand forecasting; tourism; climate change; machine learning; extreme value analysis; drinking water
Categories
Funding
- De Watergroep
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Extremes in drinking water demand are commonly quantified with a so called peaking factor, a probabilistic ratio expressing the daily water demand relative to its annual average corresponding with a once in ten year recurrence period. In this study, we present a modeling framework that allows one to quantify of the impact of climate change and variations in vacation absence on the peaking factor for specific geographic regions. The framework consists of a support vector regression model that simulates daily water demand as a function of meteorological parameters and vacation absence, coupled to an extreme value model that translates simulation results to a peaking factor. After initial model development, we simulated the effects of different climate change/vacation scenarios for 2050 on eight water supply areas in the Netherlands and Belgium. We found that on average there is a net increase in water demand of 0.8% in 2050 and a 6.5% increase in peak demand compared to the reference period.
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